Multi-objective planning model for urban greening based on optimization algorithms

Abstract

Green space is fundamental to the quality of life of residents, and as such, urban planning or improvement projects often include strategies directly or indirectly related to greening. Although green space is well established to generate positive effects such as cooling, runoff reducing, and ecological corridor, a few studies have examined the comprehensive process of greening in the urban planning context. To fill this gap in the literature, this study seeks to identify a planning model that determines the location and type of greens based on its multiple effects (e.g., cooling and enhancement of connectivity) and calculates the implementation cost using meta-heuristic optimization algorithms. We obtained 30 Pareto-optimal plans by applying our model to the hypothetical landscape at a neighborhood scale. The results showed a synergistic relationship between cooling and enhancement of connectivity, as well as a trade-off relationship between greenery effects and implementation cost. We also defined critical lots for urban greening that are commonly selected in various plans. We expect our model to contribute to the improvement of existing planning processes by repeating the positive feedback loop: from plan modification to quantitative evaluation and selection of better plans. These optimal plans can also be considered as options for "co-design" by related stakeholders.